# model settings
model = dict(
    type="CascadeRCNN",
    data_preprocessor=dict(
        type="DetDataPreprocessor",
        mean=[123.675, 116.28, 103.53],
        std=[58.395, 57.12, 57.375],
        bgr_to_rgb=True,
        pad_size_divisor=32,
    ),
    backbone=dict(
        type="ResNet",
        depth=50,
        num_stages=4,
        out_indices=(0, 1, 2, 3),
        frozen_stages=1,
        norm_cfg=dict(type="BN", requires_grad=True),
        norm_eval=True,
        style="pytorch",
        init_cfg=dict(type="Pretrained", checkpoint="torchvision://resnet50"),
    ),
    neck=dict(type="FPN", in_channels=[256, 512, 1024, 2048], out_channels=256, num_outs=5),
    rpn_head=dict(
        type="RPNHead",
        in_channels=256,
        feat_channels=256,
        anchor_generator=dict(type="AnchorGenerator", scales=[8], ratios=[0.5, 1.0, 2.0], strides=[4, 8, 16, 32, 64]),
        bbox_coder=dict(type="DeltaXYWHBBoxCoder", target_means=[0.0, 0.0, 0.0, 0.0], target_stds=[1.0, 1.0, 1.0, 1.0]),
        loss_cls=dict(type="CrossEntropyLoss", use_sigmoid=True, loss_weight=1.0),
        loss_bbox=dict(type="SmoothL1Loss", beta=1.0 / 9.0, loss_weight=1.0),
    ),
    roi_head=dict(
        type="CascadeRoIHead",
        num_stages=3,
        stage_loss_weights=[1, 0.5, 0.25],
        bbox_roi_extractor=dict(
            type="SingleRoIExtractor",
            roi_layer=dict(type="RoIAlign", output_size=7, sampling_ratio=0),
            out_channels=256,
            featmap_strides=[4, 8, 16, 32],
        ),
        bbox_head=[
            dict(
                type="Shared2FCBBoxHead",
                in_channels=256,
                fc_out_channels=1024,
                roi_feat_size=7,
                num_classes=80,
                bbox_coder=dict(
                    type="DeltaXYWHBBoxCoder", target_means=[0.0, 0.0, 0.0, 0.0], target_stds=[0.1, 0.1, 0.2, 0.2]
                ),
                reg_class_agnostic=True,
                loss_cls=dict(type="CrossEntropyLoss", use_sigmoid=False, loss_weight=1.0),
                loss_bbox=dict(type="SmoothL1Loss", beta=1.0, loss_weight=1.0),
            ),
            dict(
                type="Shared2FCBBoxHead",
                in_channels=256,
                fc_out_channels=1024,
                roi_feat_size=7,
                num_classes=80,
                bbox_coder=dict(
                    type="DeltaXYWHBBoxCoder", target_means=[0.0, 0.0, 0.0, 0.0], target_stds=[0.05, 0.05, 0.1, 0.1]
                ),
                reg_class_agnostic=True,
                loss_cls=dict(type="CrossEntropyLoss", use_sigmoid=False, loss_weight=1.0),
                loss_bbox=dict(type="SmoothL1Loss", beta=1.0, loss_weight=1.0),
            ),
            dict(
                type="Shared2FCBBoxHead",
                in_channels=256,
                fc_out_channels=1024,
                roi_feat_size=7,
                num_classes=80,
                bbox_coder=dict(
                    type="DeltaXYWHBBoxCoder",
                    target_means=[0.0, 0.0, 0.0, 0.0],
                    target_stds=[0.033, 0.033, 0.067, 0.067],
                ),
                reg_class_agnostic=True,
                loss_cls=dict(type="CrossEntropyLoss", use_sigmoid=False, loss_weight=1.0),
                loss_bbox=dict(type="SmoothL1Loss", beta=1.0, loss_weight=1.0),
            ),
        ],
    ),
    # model training and testing settings
    train_cfg=dict(
        rpn=dict(
            assigner=dict(
                type="MaxIoUAssigner",
                pos_iou_thr=0.7,
                neg_iou_thr=0.3,
                min_pos_iou=0.3,
                match_low_quality=True,
                ignore_iof_thr=-1,
            ),
            sampler=dict(type="RandomSampler", num=256, pos_fraction=0.5, neg_pos_ub=-1, add_gt_as_proposals=False),
            allowed_border=0,
            pos_weight=-1,
            debug=False,
        ),
        rpn_proposal=dict(nms_pre=2000, max_per_img=2000, nms=dict(type="nms", iou_threshold=0.7), min_bbox_size=0),
        rcnn=[
            dict(
                assigner=dict(
                    type="MaxIoUAssigner",
                    pos_iou_thr=0.5,
                    neg_iou_thr=0.5,
                    min_pos_iou=0.5,
                    match_low_quality=False,
                    ignore_iof_thr=-1,
                ),
                sampler=dict(type="RandomSampler", num=512, pos_fraction=0.25, neg_pos_ub=-1, add_gt_as_proposals=True),
                pos_weight=-1,
                debug=False,
            ),
            dict(
                assigner=dict(
                    type="MaxIoUAssigner",
                    pos_iou_thr=0.6,
                    neg_iou_thr=0.6,
                    min_pos_iou=0.6,
                    match_low_quality=False,
                    ignore_iof_thr=-1,
                ),
                sampler=dict(type="RandomSampler", num=512, pos_fraction=0.25, neg_pos_ub=-1, add_gt_as_proposals=True),
                pos_weight=-1,
                debug=False,
            ),
            dict(
                assigner=dict(
                    type="MaxIoUAssigner",
                    pos_iou_thr=0.7,
                    neg_iou_thr=0.7,
                    min_pos_iou=0.7,
                    match_low_quality=False,
                    ignore_iof_thr=-1,
                ),
                sampler=dict(type="RandomSampler", num=512, pos_fraction=0.25, neg_pos_ub=-1, add_gt_as_proposals=True),
                pos_weight=-1,
                debug=False,
            ),
        ],
    ),
    test_cfg=dict(
        rpn=dict(nms_pre=1000, max_per_img=1000, nms=dict(type="nms", iou_threshold=0.7), min_bbox_size=0),
        rcnn=dict(score_thr=0.05, nms=dict(type="nms", iou_threshold=0.5), max_per_img=100),
    ),
)
